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Features Importance

Spearman Correlation of Models

Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
1.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.352021 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.20249 |
0.10139 |
| accuracy |
0.112651 |
0.10139 |
| precision |
0.112651 |
0.10139 |
| recall |
1 |
0.10139 |
| mcc |
0 |
0.10139 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.352021 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.20249 |
0.10139 |
| accuracy |
0.112651 |
0.10139 |
| precision |
0.112651 |
0.10139 |
| recall |
1 |
0.10139 |
| mcc |
0 |
0.10139 |
Confusion matrix (at threshold=0.10139)
|
Predicted as no |
Predicted as yes |
| Labeled as no |
0 |
6853 |
| Labeled as yes |
0 |
870 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
41.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.220858 |
nan |
| auc |
0.886197 |
nan |
| f1 |
0.578797 |
0.451358 |
| accuracy |
0.907031 |
0.49107 |
| precision |
0.592233 |
0.49107 |
| recall |
1 |
0.011272 |
| mcc |
0.525154 |
0.451358 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.220858 |
nan |
| auc |
0.886197 |
nan |
| f1 |
0.576151 |
0.49107 |
| accuracy |
0.907031 |
0.49107 |
| precision |
0.592233 |
0.49107 |
| recall |
0.56092 |
0.49107 |
| mcc |
0.524228 |
0.49107 |
Confusion matrix (at threshold=0.49107)
|
Predicted as no |
Predicted as yes |
| Labeled as no |
6517 |
336 |
| Labeled as yes |
382 |
488 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 3_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
19.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.178619 |
nan |
| auc |
0.945411 |
nan |
| f1 |
0.646284 |
0.292691 |
| accuracy |
0.91726 |
0.413073 |
| precision |
0.667797 |
0.553569 |
| recall |
1 |
0.000296014 |
| mcc |
0.606032 |
0.27996 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.178619 |
nan |
| auc |
0.945411 |
nan |
| f1 |
0.642417 |
0.413073 |
| accuracy |
0.91726 |
0.413073 |
| precision |
0.625954 |
0.413073 |
| recall |
0.65977 |
0.413073 |
| mcc |
0.595935 |
0.413073 |
Confusion matrix (at threshold=0.413073)
|
Predicted as no |
Predicted as yes |
| Labeled as no |
6510 |
343 |
| Labeled as yes |
296 |
574 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 4_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
19.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.203019 |
nan |
| auc |
0.935024 |
nan |
| f1 |
0.613613 |
0.360931 |
| accuracy |
0.908585 |
0.516128 |
| precision |
0.692308 |
0.556366 |
| recall |
1 |
9.68502e-21 |
| mcc |
0.568529 |
0.218837 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.203019 |
nan |
| auc |
0.935024 |
nan |
| f1 |
0.474702 |
0.516128 |
| accuracy |
0.908585 |
0.516128 |
| precision |
0.672996 |
0.516128 |
| recall |
0.366667 |
0.516128 |
| mcc |
0.453202 |
0.516128 |
Confusion matrix (at threshold=0.516128)
|
Predicted as no |
Predicted as yes |
| Labeled as no |
6698 |
155 |
| Labeled as yes |
551 |
319 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

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Summary of 5_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
18.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.202325 |
nan |
| auc |
0.926093 |
nan |
| f1 |
0.616172 |
0.337384 |
| accuracy |
0.913635 |
0.479097 |
| precision |
0.636977 |
0.479097 |
| recall |
1 |
0.00932094 |
| mcc |
0.565795 |
0.337384 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.202325 |
nan |
| auc |
0.926093 |
nan |
| f1 |
0.585971 |
0.479097 |
| accuracy |
0.913635 |
0.479097 |
| precision |
0.636977 |
0.479097 |
| recall |
0.542529 |
0.479097 |
| mcc |
0.540265 |
0.479097 |
Confusion matrix (at threshold=0.479097)
|
Predicted as no |
Predicted as yes |
| Labeled as no |
6584 |
269 |
| Labeled as yes |
398 |
472 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 3_Default_Xgboost |
4 |
| 4_Default_NeuralNetwork |
1 |
Metric details
|
score |
threshold |
| logloss |
0.177428 |
nan |
| auc |
0.94601 |
nan |
| f1 |
0.646399 |
0.281485 |
| accuracy |
0.916872 |
0.506236 |
| precision |
0.670788 |
0.520433 |
| recall |
1 |
0.000236811 |
| mcc |
0.606017 |
0.281485 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.177428 |
nan |
| auc |
0.94601 |
nan |
| f1 |
0.587404 |
0.506236 |
| accuracy |
0.916872 |
0.506236 |
| precision |
0.666181 |
0.506236 |
| recall |
0.525287 |
0.506236 |
| mcc |
0.546633 |
0.506236 |
Confusion matrix (at threshold=0.506236)
|
Predicted as no |
Predicted as yes |
| Labeled as no |
6624 |
229 |
| Labeled as yes |
413 |
457 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

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